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Pharmacy Claims Fraud Detection Using Apache Spark
⏲ 22 min 43 sec ✓ 11-Jun-2018
154 - Understanding the training and validation loss curves
⏲ 27 minutes 47 seconds 👁 49.8K
House Price Prediction in Python - Full Machine Learning Project
⏲ 40 minutes 40 seconds 👁 104K
Machine Learning for the Apache Spark Developer Paige Lu (Microsoft)
⏲ 18 min 12 sec ✓ 11-Jun-2018
Types Of Machine Learning | Machine Learning Algorithms | Machine Learning Tutorial | Simplilearn
⏲ 21 minutes 9 seconds 👁 122.3K
10 ML algorithms in 45 minutes | machine learning algorithms for data science | machine learning
⏲ 46 minutes 18 seconds 👁 122.5K
Dr. W. Hsu presented \
⏲ 42 min 68 sec ✓ 17-Feb-2023
Machine Learning vs.Deep Learningvs.Foundation Models
⏲ 7 minutes 27 seconds 👁 23.9K
Deploying ML Models in Production: An Overview
⏲ 14 minutes 27 seconds 👁 19.1K
Interpretable Question Answering on Knowledge Bases and Text
⏲ 8 min 12 sec ✓ 16-Jan-2020
Machine Learning: Testing and Error Metrics
⏲ 44 minutes 43 seconds 👁 53.3K
Evaluating Machine Learning Models
⏲ 8 minutes 7 seconds 👁 3.8K
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_pharmacy-claims-fraud-detection-using-apache-spark.webp "Tuning a Spark ML model with cross-validation can be an extremely computationally expensive process. As the number of hyperparameter combinations increases, so does the number of models being evaluated. The default configuration in Spark is to evaluate each of these models one-by-one to select the best performing. When running this process with a large number of models, if the training and evaluation of a model does not fully utilize the available cluster resources then that waste will be compou")
_154-understanding-the-training-and-validation-loss-curves.webp "DigitalSreeni")
_house-price-prediction-in-python-full-machine-learning-project.webp "NeuralNine")
_machine-learning-for-the-apache-spark-developer-paige-lu-microsoft.webp "Developers learning Apache Spark are often times quickly impressed by Apache Spark’s powerful capability to do large scale ETL and Streaming in a simple and elegant way. However learning to use Spark effectively in Machine Learning is still challenging, not only because ML introduces many new concepts outside of computer science, but also because:nn• Many ML tutorials and samples are written in R or Pythonnn• Many Spark ML examples and online classes use Spark versions prior to 2.0 and the")
_types-of-machine-learning-124-machine-learning-algorithms-124-machine-learning-tutorial-124-simplilearn.webp "Simplilearn")
_10-ml-algorithms-in-45-minutes-124-machine-learning-algorithms-for-data-science-124-machine-learning.webp "Unfold Data Science")
_dr-w-hsu-presented-92.webp "Dr. W. Hsu presented a lecture in the Fundamental Concepts to AI/ML (AIML) Module on")
_machine-learning-vs-deep-learning-vs-foundation-models.webp "IBM Technology")
_deploying-ml-models-in-production-an-overview.webp "Valerio Velardo - The Sound of AI")
_interpretable-question-answering-on-knowledge-bases-and-text.webp "Authors: Alona Sydorova, Nina Poerner, Benjamin RothnnInterpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents. We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model. For this purpose, we propose an aut")
_machine-learning-testing-and-error-metrics.webp "Serrano.Academy")
_evaluating-machine-learning-models.webp "ritvikmath")
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